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fix: wrap eval_env in Monitor 🐛

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Roberto Schiavone
2023-06-01 23:00:00 +02:00
parent 2595a52462
commit 9802c0b6b4
1 changed files with 6 additions and 4 deletions
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10
notebooks/unit1/unit1.ipynb
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@@ -338,8 +338,9 @@
"from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.\n",
"\n",
"from stable_baselines3 import PPO\n",
"from stable_baselines3.common.env_util import make_vec_env\n",
"from stable_baselines3.common.evaluation import evaluate_policy\n",
"from stable_baselines3.common.env_util import make_vec_env"
"from stable_baselines3.common.monitor import Monitor"
]
},
{
@@ -738,6 +739,7 @@
},
"source": [
"## Evaluate the agent 📈\n",
"- Remember to wrap the environment in a [Monitor](https://stable-baselines3.readthedocs.io/en/master/common/monitor.html).\n",
"- Now that our Lunar Lander agent is trained 🚀, we need to **check its performance**.\n",
"- Stable-Baselines3 provides a method to do that: `evaluate_policy`.\n",
"- To fill that part you need to [check the documentation](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html#basic-usage-training-saving-loading)\n",
@@ -784,7 +786,7 @@
"outputs": [],
"source": [
"#@title\n",
"eval_env = gym.make(\"LunarLander-v2\")\n",
"eval_env = Monitor(gym.make(\"LunarLander-v2\"))\n",
"mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
"print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
]
@@ -917,7 +919,7 @@
"env_id = \n",
"\n",
"# Create the evaluation env and set the render_mode=\"rgb_array\"\n",
"eval_env = DummyVecEnv([lambda: gym.make(env_id, render_mode=\"rgb_array\")])\n",
"eval_env = DummyVecEnv([lambda: Monitor(gym.make(env_id, render_mode=\"rgb_array\"))])\n",
"\n",
"\n",
"# TODO: Define the model architecture we used\n",
@@ -1096,7 +1098,7 @@
"cell_type": "code",
"source": [
"#@title\n",
"eval_env = gym.make(\"LunarLander-v2\")\n",
"eval_env = Monitor(gym.make(\"LunarLander-v2\"))\n",
"mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)\n",
"print(f\"mean_reward={mean_reward:.2f} +/- {std_reward}\")"
],